الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
This study discusses the recent intelligent techniques and algorithms used for medical image visualization. These techniques cover filtering, segmentation, classification and visualization. The study started with analysis of the various medical image technologies used for acquiring medical data sets. Then was followed by discussing the advantage and disadvantage of the current approaches used to reduce the medical image noise. Subsequently for improving the analysis of an image, segmentation techniques were illustrated and compared, showing that the hybrid techniques are the best approach for medical image segmentation. Image classification approaches and their methods were presented. Additionally the technical aspects of the current Toolkits and software that are used for visualization is presented and discussed.
Now its time for mobile, mobile phones gained high performance due to the rapid development in their hardware which live up to the laptop’s hardware with the advantage of their mobility. The rapid development made mobile phones available for the heavy implementation of medical visualization techniques. Despite these developments, the stores of mobile suffer from the rare existence of medical volume visualization application. In this study a mobile application is represented to help in the orthopedic field. The implemented application shown high visualization result when compare to ImageVis3D.
New graphic application programming interfaces (APIs) have been developed as worthy candidates to support the volume rendering core. This study project a light on the performance gained by Metal API over OpenGL ES API by implemented texture mapping and compare the rendering time.
6.2 Conclusion
This thesis presented the theory behind visualization for medical images by researching the basic algorithms created in each step of visualization pipeline. Compare the techniques and algorithms used in image datasets, filter, segment, classification and visualization and illustrate the advantage and disadvantage of each algorithm. Existing research literature for mobile volume renderers is covered. Also the softwares used for rendering is covered as well.
As now its time for mobile. This thesis proposed mobile system that visualize CT medical image and the result shows that the proposed system offer visualize object more details and clear than the object obtain from ImageVis3D.
This thesis also presented the API used in mobile with a comparison between OpenGLES and Metal API and the result showed that Metal offer less rendering time when compare to OpenGLES, OpenGLES when used to render Diaphysis object offered 2.18 sec for rendering time while Metal offered 1.67, which will help to decrease the time needed for rendering.
6.3 Future work
For the future development there are several areas of focus. The mainly area will be on visualization for big data. The concept of “big data” is not new; however the way it is defined is constantly changing. Various attempts at defining big data essentially characterize it as a collection of data elements whose size, speed, type, and/or complexity require one to seek, adopt, and invent new hardware and software mechanisms in order to successfully store, analyze, and visualize the data.